code: def forward(self, local_tokens, region_tokens):
      device = local_tokens.device
      lh, lw = local_tokens.shape[-2:]
      rh, rw = region_tokens.shape[-2:]
      window_size_h, window_size_w = lh // rh, lw // rw
  
      local_tokens = rearrange(local_tokens, "b c h w -> b (h w) c")
      region_tokens = rearrange(region_tokens, "b c h w -> b (h w) c")
  
      # calculate local relative positional bias
  
      h_range = torch.arange(window_size_h, device=device)
      w_range = torch.arange(window_size_w, device=device)
  
      grid_x, grid_y = torch.meshgrid(h_range, w_range, indexing="ij")
      grid = torch.stack((grid_x, grid_y))
      grid = rearrange(grid, "c h w -> c (h w)")
      grid = (grid[:, :, None] - grid[:, None, :]) + (
          self.window_size - 1
      )
      bias_indices = (
          grid
          * torch.tensor([1, self.window_size * 2 - 1], device=device)[
              :, None, None
          ]
      ).sum(dim=0)
      rel_pos_bias = self.local_rel_pos_bias(bias_indices)
      rel_pos_bias = rearrange(rel_pos_bias, "i j h -> () h i j")
      rel_pos_bias = F.pad(rel_pos_bias, (1, 0, 1, 0), value=0)
  
      # go through r2l transformer layers
  
      for attn, ff in self.layers:
          region_tokens = attn(region_tokens) + region_tokens
  
          # concat region tokens to local tokens
  
          local_tokens = rearrange(
              local_tokens, "b (h w) d -> b h w d", h=lh
          )
          local_tokens = rearrange(
              local_tokens,
              "b (h p1) (w p2) d -> (b h w) (p1 p2) d",
              p1=window_size_h,
              p2=window_size_w,
          )
          region_tokens = rearrange(
              region_tokens, "b n d -> (b n) () d"
          )
  
          # do self attention on local tokens, along with its regional token
  
          region_and_local_tokens = torch.cat(
              (region_tokens, local_tokens), dim=1
          )
          region_and_local_tokens = (
              attn(region_and_local_tokens, rel_pos_bias=rel_pos_bias)
              + region_and_local_tokens
          )
  
          # feedforward
  
          region_and_local_tokens = (
              ff(region_and_local_tokens) + region_and_local_tokens
          )
  
          # split back local and regional tokens
  
          region_tokens, local_tokens = (
              region_and_local_tokens[:, :1],
              region_and_local_tokens[:, 1:],
          )
          local_tokens = rearrange(
              local_tokens,
              "(b h w) (p1 p2) d -> b (h p1 w p2) d",
              h=lh // window_size_h,
              w=lw // window_size_w,
              p1=window_size_h,
          )
          region_tokens = rearrange(
              region_tokens, "(b n) () d -> b n d", n=rh * rw
          )
  
      local_tokens = rearrange(
          local_tokens, "b (h w) c -> b c h w", h=lh, w=lw
      )
      region_tokens = rearrange(
          region_tokens, "b (h w) c -> b c h w", h=rh, w=rw
      )
      return local_tokens, region_tokens
  
imports: import torch
  from torch import nn, einsum
  from einops import rearrange
  from einops.layers.torch import Rearrange, Reduce
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the R2LTransformer obj's underlying member method code with example usage: obj.forward(local_tokens, region_tokens) and returns: local_tokens,  region_tokens